Computation of Air Traffic Flow Management Performance with Long Short-Term Memories Considering Weather Impact
Published in ICANN 2018. Bd. 11140. Lecture Notes in Computer Science. Springer, 2018
Recommended citation: S. Reitmann und M. Schultz. “Computation of Air Traffic Flow Management Performance with Long Short-Term Memories Considering Weather Impact”. In: Artificial Neural Networks and Machine Learning – ICANN 2018. Bd. 11140. Lecture Notes in Computer Science. Springer, 2018, S. 532–541. ISBN: 978-3-030-01421-6. DOI: 10.1007/978-3-030-01421-6_51. http://dx.doi.org/10.1007/978-3-030-01421-6_51
In this paper we compute the impact of weather events to airport performance, which is measured as deviation of actual and scheduled timestamps (delay). Weather phenomena are categorized by the Air Traffic Management Airport Performance weather algorithm, which aims to quantify weather conditions at European airports. A comprehensive dataset of flights of 2013 for example airport Hamburg and accompanied weather data result in both a quantification of the individual airport performance and an aggregated weather-performance metric.
To model complex correlations between weather and flight schedule data we use advance machine learning procedures as Long Short-Term Memories are. Various structured models are applied to certain simulation scenarios considering differences in weather affected air traffic dynamics.